基于车致振动响应的铁路桥梁损伤识别方法研究
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摘要
处于复杂环境中的铁路桥梁结构尺寸大、刚度大且承受的活载大,这使得结构的损伤状态中存在着与荷载的相关性。现有损伤识别方法未考虑这种相关性,所以将这些方法应用到铁路桥梁结构损伤识别中时,存在很多局限性。为此,结合研究小样本情况下机器学习规律的统计学习理论,对列车荷载作用下桥梁结构的损伤识别进行专门研究,并完成了以下几个方面的工作：
1、针对与列车荷载相关的损伤,提出了铁路桥梁结构损伤识别的子区域法：首先通过结构易损性分析,确定结构易损部位；再根据易损部位的损伤状态,从列车在桥梁上行驶的时间区域中选择出若干子区域；然后在每个子区域内,假定所研究的易损部位的损伤状态保持不变,进行损伤识别研究。
2、针对结构损伤程度与荷载的相关性,采用5步策略分析每个子区域内的损伤识别：损伤预警、损伤等级划分、损伤位置识别、损伤程度识别和损伤精确识别。
3、由于结构完好状态和损伤状态之间有较大的区别,所以将损伤预警看作统计学中的模式识别问题；以加速度时程数据构建损伤指标,优化样本库,然后利用统计学习理论中支持向量机分类算法建立损伤预警模型。将所提方法应用到一个三跨连续梁数值模型,结果表明：当测试数据对应的列车模型与训练样本对应的相接近时,损伤预警识别模型具有较好的区分能力和一定的抗噪声能力。
4、由于不同损伤等级的结构状态之间存在较大的差别,可将损伤等级划分看成模式识别问题,利用统计学习理论的支持向量机分类算法,判断测试结构处于较差、差的和危险3个等级中的哪一个等级。针对一个三跨连续梁,建立损伤等级划分模型,并利用不同的测试数据检验该模型,结果表明：所提出的方法能够将待测的结构状态划分到正确的等级中。
5、针对损伤位置识别的特点,提出了损伤位置识别的分层法：首先识别出损伤所在的区间,然后识别所在的子区间；再将每一层中的位置识别看成一个统计学中的模式识别问题,利用统计学习理论中的支持向量机分类算法建立损伤位置识别模型；然后针对该模型,构建适合的位置识别指标,优化样本库。最后使用三跨连续梁数值模型检验所提出的方法,结果表明：该方法能够较准确地识别出损伤发生的位置,而且有一定的抗噪声能力。
6、由于损伤程度是连续变化的,可将损伤程度识别问题看成统计学中的回归估计问题,以加速度时程数据所构建的指标作为自变量,以损伤程度作为因变量,利用统计学习理论中的支持向量机回归算法进行求解。三跨连续梁桥的数值试验结果表明：该方法能够较准确地识别出损伤所在子区间的整体损伤程度,但结果具有一定离散性。
7、针对损伤精确识别的特点,提出了损伤精确识别的方法：在某一特定损伤子区间的损伤程度识别基础上,将该子区间分为2个区段,以其中一个区段作为待求区段,并将其损伤程度作为识别目标,将损伤精确识别问题看成是统计学中的回归估计问题,利用统计学习理论中的支持向量机回归算法,识别待求区段的损伤程度；再根据子区间整体损伤程度的一致性,推算出另一个区段的损伤程度。将所提方法应用到三跨连续梁桥模型中,结果表明：该方法能够较准确地识别出待求区段的损伤程度,但结果具有一定的离散性。
Since the railway bridge structures in the complex environment have a large size, possess a strong stiffness, and take a heavy live load, some bridge damage states correlate with the live load. However, this correlation isn't considered by existing methods for damage detection, so when these methods are applied to the real railway bridge structures, many limitations are found. Therefore, by using statistical learning theory, which studies the law of machine learning while the number of samples is small, damage detection of railway bridge structure is studied under train, and the following works have been done:
Firstly, aiming to the bridge structural damage states which correlate with train load, the time subdomain method for bridge structural damage detection is put forward. In this method, the structural damage vulnerability is analyzed to find out the vulnerable sections firstly. Then owing to the damage state of the vulnerable sections, right subdomains are selected from the time domain when the train is running on railway bridge. Based on the suppose that the damge state of the studied sections remains the same in a subdomain, damage detection is studied.
Secondly, according to the correlation between damage degree with load value, five-step strategy is adopted to study the damage detection in every subdomain:damage alarming, division of damage grade, identification of damage location, recognition of damage degree, and precise damage detection.
Thirdly, damage alarming is viewed as a problem of pattern recognition since there are a lot of differences between damage state and healthy state. Acceleration time history is used to construct damage index, sample set is optimized, and the classified algorithm, which is carried out by support vector machine in statistical learning theory, is applied to establish a model of damage alarming. At last, the proposed method for damage alarming is applied to a three-span continuous beam, and the result shows that when the train model of test data is close to the model of the sample, the damage alarming model has a preferable capability of identification and noise resistance.
Fourthly, as structural states in different damage grades are distinct, grade division is viewed as a problem of pattern recognition in statistics, and the damage grade of the test structure, which is one of the three grades:inferior, poor and dangerous grades, can be judged by applying support vector machine to classification. A model of grade division is erected according to a three-span continuous bearn, and some different data are used to examine the model. At last, the result shows that the test structure can be classified rightly by this method.
Fifthly, based on the characteristics of damage location identification, the hierarchical method has been introduced:the zone where the damage is located is estimated firstly, and then the subzone is identified. In every step, location identification is viewed as a problem of pattern recognition in statistics, and support vector classification is used to establish a model of damage location identification, and then proper index of location identification is constructed, and sample set is optimized to match the model. At last, the example of continuous beam is used to test the proposed method for damage location identification, and the result shows that the model can identify the damage location exactly, and has an appropriate capability of noise resistance.
Sixthly, because damage degrees vary continuously, damage degree recognition is regarded as a problem of regression estimation. The index, which is constructed by acceleration time history, is taken as independent variable, and damage degree is regarded as dependent variable. Then support vector regression is applied to solve the problem. The result of the three-span continuous beam test shows that this method can recognize the gobal damage degree of the subzone where the damage is located, but the result has some discreteness.
Lastly, based on the characteristics of precise damage detection, a method for precise detection is proposed:after damage degree recognition, the damage subzone is divided into two sections, and one of the sections is viewed as a solving section, of which damage degree is the detection target. By regarding damage degree recognition as a problem of regression estimation, support vector regression is used to recognize the damage degree of the solving section. Then the damage degree of another section is calculated according to the consistency of the subzone global damage degree. The proposed method is applied to the continuous beam, and the result shows that the damage degree of the solving section can be recognized by this method, but the calculated result has some discreteness.
1、针对与列车荷载相关的损伤,提出了铁路桥梁结构损伤识别的子区域法：首先通过结构易损性分析,确定结构易损部位；再根据易损部位的损伤状态,从列车在桥梁上行驶的时间区域中选择出若干子区域；然后在每个子区域内,假定所研究的易损部位的损伤状态保持不变,进行损伤识别研究。
2、针对结构损伤程度与荷载的相关性,采用5步策略分析每个子区域内的损伤识别：损伤预警、损伤等级划分、损伤位置识别、损伤程度识别和损伤精确识别。
3、由于结构完好状态和损伤状态之间有较大的区别,所以将损伤预警看作统计学中的模式识别问题；以加速度时程数据构建损伤指标,优化样本库,然后利用统计学习理论中支持向量机分类算法建立损伤预警模型。将所提方法应用到一个三跨连续梁数值模型,结果表明：当测试数据对应的列车模型与训练样本对应的相接近时,损伤预警识别模型具有较好的区分能力和一定的抗噪声能力。
4、由于不同损伤等级的结构状态之间存在较大的差别,可将损伤等级划分看成模式识别问题,利用统计学习理论的支持向量机分类算法,判断测试结构处于较差、差的和危险3个等级中的哪一个等级。针对一个三跨连续梁,建立损伤等级划分模型,并利用不同的测试数据检验该模型,结果表明：所提出的方法能够将待测的结构状态划分到正确的等级中。
5、针对损伤位置识别的特点,提出了损伤位置识别的分层法：首先识别出损伤所在的区间,然后识别所在的子区间；再将每一层中的位置识别看成一个统计学中的模式识别问题,利用统计学习理论中的支持向量机分类算法建立损伤位置识别模型；然后针对该模型,构建适合的位置识别指标,优化样本库。最后使用三跨连续梁数值模型检验所提出的方法,结果表明：该方法能够较准确地识别出损伤发生的位置,而且有一定的抗噪声能力。
6、由于损伤程度是连续变化的,可将损伤程度识别问题看成统计学中的回归估计问题,以加速度时程数据所构建的指标作为自变量,以损伤程度作为因变量,利用统计学习理论中的支持向量机回归算法进行求解。三跨连续梁桥的数值试验结果表明：该方法能够较准确地识别出损伤所在子区间的整体损伤程度,但结果具有一定离散性。
7、针对损伤精确识别的特点,提出了损伤精确识别的方法：在某一特定损伤子区间的损伤程度识别基础上,将该子区间分为2个区段,以其中一个区段作为待求区段,并将其损伤程度作为识别目标,将损伤精确识别问题看成是统计学中的回归估计问题,利用统计学习理论中的支持向量机回归算法,识别待求区段的损伤程度；再根据子区间整体损伤程度的一致性,推算出另一个区段的损伤程度。将所提方法应用到三跨连续梁桥模型中,结果表明：该方法能够较准确地识别出待求区段的损伤程度,但结果具有一定的离散性。
Since the railway bridge structures in the complex environment have a large size, possess a strong stiffness, and take a heavy live load, some bridge damage states correlate with the live load. However, this correlation isn't considered by existing methods for damage detection, so when these methods are applied to the real railway bridge structures, many limitations are found. Therefore, by using statistical learning theory, which studies the law of machine learning while the number of samples is small, damage detection of railway bridge structure is studied under train, and the following works have been done:
Firstly, aiming to the bridge structural damage states which correlate with train load, the time subdomain method for bridge structural damage detection is put forward. In this method, the structural damage vulnerability is analyzed to find out the vulnerable sections firstly. Then owing to the damage state of the vulnerable sections, right subdomains are selected from the time domain when the train is running on railway bridge. Based on the suppose that the damge state of the studied sections remains the same in a subdomain, damage detection is studied.
Secondly, according to the correlation between damage degree with load value, five-step strategy is adopted to study the damage detection in every subdomain:damage alarming, division of damage grade, identification of damage location, recognition of damage degree, and precise damage detection.
Thirdly, damage alarming is viewed as a problem of pattern recognition since there are a lot of differences between damage state and healthy state. Acceleration time history is used to construct damage index, sample set is optimized, and the classified algorithm, which is carried out by support vector machine in statistical learning theory, is applied to establish a model of damage alarming. At last, the proposed method for damage alarming is applied to a three-span continuous beam, and the result shows that when the train model of test data is close to the model of the sample, the damage alarming model has a preferable capability of identification and noise resistance.
Fourthly, as structural states in different damage grades are distinct, grade division is viewed as a problem of pattern recognition in statistics, and the damage grade of the test structure, which is one of the three grades:inferior, poor and dangerous grades, can be judged by applying support vector machine to classification. A model of grade division is erected according to a three-span continuous bearn, and some different data are used to examine the model. At last, the result shows that the test structure can be classified rightly by this method.
Fifthly, based on the characteristics of damage location identification, the hierarchical method has been introduced:the zone where the damage is located is estimated firstly, and then the subzone is identified. In every step, location identification is viewed as a problem of pattern recognition in statistics, and support vector classification is used to establish a model of damage location identification, and then proper index of location identification is constructed, and sample set is optimized to match the model. At last, the example of continuous beam is used to test the proposed method for damage location identification, and the result shows that the model can identify the damage location exactly, and has an appropriate capability of noise resistance.
Sixthly, because damage degrees vary continuously, damage degree recognition is regarded as a problem of regression estimation. The index, which is constructed by acceleration time history, is taken as independent variable, and damage degree is regarded as dependent variable. Then support vector regression is applied to solve the problem. The result of the three-span continuous beam test shows that this method can recognize the gobal damage degree of the subzone where the damage is located, but the result has some discreteness.
Lastly, based on the characteristics of precise damage detection, a method for precise detection is proposed:after damage degree recognition, the damage subzone is divided into two sections, and one of the sections is viewed as a solving section, of which damage degree is the detection target. By regarding damage degree recognition as a problem of regression estimation, support vector regression is used to recognize the damage degree of the solving section. Then the damage degree of another section is calculated according to the consistency of the subzone global damage degree. The proposed method is applied to the continuous beam, and the result shows that the damage degree of the solving section can be recognized by this method, but the calculated result has some discreteness.
引文
[1]J.M. Ko, Y.Q. Ni. Technology developments in structural health monitoring of large-scale bridges. Engineering Structures,2005,27:1715-1725.
[2]黄方林,王学敏,陈政清,等.大型桥梁健康监测研究进展.中国铁道科学,2005,26(2)：1-7.
[3]Darryll Pines, A Emin Aktan. Status of structural health monitoring of long-span bridges in the United States. Progress in Structural Engineering and Materials,2002,4: 372-380.
[4]Charles R Farrar, David A Jauregui. Comparative study of damage identification algorithms applied to a bridge:Ⅰ. Experiment. Smart Materials and Structures,1998, 7(5):704-719.
[5]Charles R Farrar, David A Jauregui. Comparative study of damage identification algorithms applied to a bridge:Ⅱ. Numerical study. Smart Materials and Structures, 1998,7(5):720-731.
[6]Catbas F. Necati, Aktan A Emin. Condition and damage assessment:issues and some promising indices. Journal of structural engineering,2002,128(8):1026-1036.
[7]Catbas F. Necati, Melih Susoy, Dan M. Frangopol. Structural health monitoring and reliability estimation:Long span truss bridge application with environmental monitoring data. Engineering Structures,2008,30:2347-2359.
[8]Bart Peeters, Guido De Roeck. One-year monitoring of the Z24-Bridge:environmental effects versus damage events. Earthquake Engineering & Structural Dynamics,2001, 30:149-171.
[9]Bart Peeters, Johan Maeck, Guido De Roeck. Dynamic monitoring of the z24-bridge: separating temperature effects from damage. In proceedings of the European COST F3 Conference on System Identification and Structural Health monitoring, Madrid, Spain, June 2000.377-386.
[10]C. Kramer, C. A. M. de Smet, G. de Roeck. Z24 Bridge Damage Detection Tests. Proceedings of the 1999 17th International Modal Analysis Conference, Orlando FL, 1999:1023-1029.
[11]J.B. Frandsen. Simultaneous pressures and accelerations measured full-scale on the Great Belt East suspension bridge. Journal of Wind Engineering and Industrial Aerodynamics,2001,89:95-129.
[12]Yozo Fujino. Vibration, control and monitoring of long-span bridges—recent research, developments and practice in Japan. Journal of Constructional Steel Research,2002, 58:71-97.
[13]Jinping Ou, H.M. Koh, S. Kim, Wolfgang Habel, et al. Sensing Issues in Civil Structural Health Monitoring. Springer Netherlands,2005.
[14]Akira Mita. Emerging needs in Japan for health monitoring technologies in civil and building structures.2nd International Workshop on Structural Health Monitoring, USA, Stanford University, Sept,1999.1-12.
[15]Chung-Bang Yun, Jong-Jae Lee, Sung-Kon Kim, et al. Recent R&D Activities on Structural Health Monitoring for Civil Infra-Structures in Korea. KSCE Journal of Civil Engineering,2003,7(6):637-651.
[16]Sung-Kon Kim. Intelligent Health Monitoring System for a Cable-Stayed Bridge. KSCE Journal of Civil Engineering,2001,5:37-41.
[17]李宏男,高东伟,伊廷华.土木工程结构健康监测系统的研究状况与进展.力学进展,2008,38(2)：151-166.
[18]朱桂新,陈旭东,王迎军GPS RTK技术在虎门大桥运营安全监测中的应用.公路,2002,7：55-58.
[19]邱法维,杜文博,钱稼茹.虎门大桥应变监测数据处理系统设计.桥梁建设,2003,2：66-69.
[20]缪长青,李爱群,冯兆祥等.润扬大桥结构健康监测系统设计研究.世界桥梁,2006,3：63-66.
[21]李爱群,丁幼亮.工程结构损伤预警理论及其应用.北京：科学出版社,2007.
[22]张敏,杨志芳,朱利明等.东海大桥桥梁结构健康监测系统研究与设计.桥梁建设,2006,2：67-70.
[23]牛进民,吕令红,李金仙等.东明黄河公路大桥健康与安全监测的研究及应用.华东公路,2009,2：80-83.
[24]朱绍玮,张宇峰,樊可清等.江阴大桥结构健康监测系统升级改造及初步数据分析.公路,2007,1：69-73.
[25]何旭辉,陈政清,黄方林.南京长江大桥结构健康监测及其关键技术研究.中国铁道科学,2008,29(4)：138-140.
[26]余波,邱洪兴,王浩等.苏通大桥结构健康监测系统设计.地震工程与工程振动.2009,29(4)：170-177.
[27]苏木标,杜彦良,孙宝臣等.芜湖长江大桥长期健康监测与报警系统研究.铁道学报,2007,29(2)：71-76.
[28]宗周红,孙建林,徐立群等.下白石大桥健康监测系统的设计与研究.铁道学报,2008,30(5)：65-71.
[29]曹映泓Ming L. Wang,朱利明.湛江海湾大桥分布式健康监测系统开发研究.公路,2007,1：56-62.
[30]王俊如,阵伟民,刘纲等.重庆石板坡长江大桥复线桥的受力分析及健康监测系统设计.现代交通技术,2008,5(5)：37-40.
[31]单德山,李乔,付春雨,严鹏.智能桥梁健康监测与损伤评估.北京：人民交通出版社,2010.
[32]刘正光,黄钊猷.香港缆索桥结构健康监测系统.市政技术,2005,23(z1)：22-25.
[33]李惠彬,秦权,钱良忠.青马悬索桥的时域模态识别.土木工程学报,2001,34(5)：52-57.
[34]王柏生,倪一清,高赞明.青马大桥桥板结构损伤位置识别的数值模拟.土木工程学报,34(3)：67-73.
[35]Olof G. Gustafsson; Tibor Tallian. Detection of Damage in Assembled Rolling Element Bearings. Tribology Transactions,1962,5(1):197-209.
[36]杨智春,于哲峰.结构健康监测中的损伤检测技术研究进展.力学进展,2004,34(2)：215-223.
[37]Shih-Chi Liu, James T.P. Yao. Structural Identification Concept. Journal of the Structural Division,1978,104(12):1848-1858.
[38]谢献忠,易伟建,王修勇,等.结构损伤诊断与系统时域辨识研究综述.中国安全科学学报,2008,18(6)：110-115.
[39]杨秋伟,基于振动的结构损伤识别方法研究进展.振动和冲击,2007,26(10)：86-91.
[40]Scott W. Doebling, Charles R. Farrar, Michael B. Prime. A summary review of vibration-based damage identification methods. The Shock and vibration digest,1998, 30:91-105.
[41]D. Montalvao, N.M.M. Maia, A.M.R. Ribeiro. A Review of Vibration-based Structural Health Monitoring with Special Emphasis on Composite Materials. The Shock and vibration digest,2006,38:295-324.
[42]Spilios D Fassois, John S Sakellariou. Time-series methods for fault detection and identification in vibrating structures. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences,2007,365:411-448.
[43]Banan M R, Hjelmstad K D. Parameter Estimation of Structures from Static response I:Computational Aspects. Journal of Structural Engineering, ASCE,1994,120 (11): 3243-3258.
[44]Banan M R, Hjelmstad K D. Parameter Estimation of Structures from Static Response Ⅱ:Numerical Simulation Studies[J].Journal of Structural Engineering, ASCE,1994, 120(11):3259-3283.
[45]崔飞,杨党旗,高岩.用参数识别技术进行桥梁结构损伤识别.中国铁道科学,2004,24(3)：36.
[46]向天宇,赵人达,刘海波.基于静力测试数据的预应力混凝土连续梁结构损伤识别. 土木工程学报,2003,36(11)：79-82.
[47]付春雨,李乔,单德山.基于位移连续的静力损伤识别,桥梁建设,2010,2：18-20.
[48]Gaetan Kerschen, Keith Worden, Alexander F. Vakakis, et al. Past, present and future of nonlinear system identification in structural dynamics. Mechanical Systems and Signal Processing,2006,20:505-592.
[49]Y.J. Yan, L. Cheng, Z.Y. Wu. et al. Development in vibration-based structural damage detection technique. Mechanical Systems and Signal Processing,2007,21:2198-2211.
[50]秦仙蓉,张令弥,顾明等.改进的基于模态参数的结构计算模型修正算法.同济大学学报,2002,30(11)：1295-1999.
[51]张刚刚,徐岳.桥梁结构损伤识别的动力指纹分析方法[J].中外公路.2005,25(2)：61-63.
[52]Hoon Sohn, Charles R. Farrar. Statistical Process Control and Projection Techniques for Damage Detection. European COST F3 Conference on System Identification and Structural Health Monitoring, Madrid, Spain,2000,105-114.
[53]姜增国,张桢.结构损伤检测类MAC应变频响函数识别指标的研究.交通科技,2006,1,7-9.
[54]冉志红.桥梁结构损伤识别的动力指纹方法研究.成都：西南交通大学博士研究生学位论文.2007.
[55]张育智.基于神经网络与数据融合的结构损伤识别理论研究.成都：西南交通大学博士研究生学位论文.2007.
[56]陈晓强.基于能量指标的结构损伤识别理论与试验研究.武汉：华中科技大学博士研究生学位论文.2009.
[57]西南交通大学土木工程学院.铁道部科技研究开发计划重大课题“智能化桥梁结构研究”结题报告-损伤识别分册.西南交通大学.2009.
[58]李国强,李杰.工程结构动力检测与应用.科学出版社,2002.
[59]Peter Van Overschee, Bart De Moor. Subspace algorithms for the stochastic identification problem. Proceedings of the 30th Conference on Decision and Control, Brighton, England,1991,1321-1326.
[60]S. Alampalli. Effects of testing, analysis, damage, and environment on modal parameters. Mechanical Systems and Signal Processing,2000,14(1):63-74.
[61]Hoon Sohn. Effects of environmental and operational variability on structural health monitoring, Philosophical Transactions of the Royal Society,2007,365:539-560.
[62]H. Sohn, M. Dzwonczyk, E.G. Straser, et al. An experimental study of temperature effect on modal parameters of the Alamosa Canyon Bridge. Earthquake Engineering & Structural Dynamics,1999,28:879-897.
[63]谢献忠,易伟建,刘锡军等.非线性时域识别方程的不适定性与正则化方法研究. 振动与冲击.2006,25(5)：120-124.
[64]X. Q. Zhu, S. S. Law. Moving Loads Identification through Regularization. Journal of engineering mechanics,2002,128(9):989-1000.
[65]X. Q. Zhu, S. S. Law. Damage Detection in Simply Supported Concrete Bridge Structure under Moving Vehicular Loads. Journal of vibration and acoustics.2007, 129(1):58-63.
[66]Z.R. Lu, S.S. Law. Identification of system parameters and input force from output only. Mechanical Systems and Signal Processing,2007,21:2099-2111.
[67]Z.R. Lu, S. S. Law. Differentiating damage effects in a structural component from the time response. Mechanical Systems and Signal Processing, Available online 28 April 2010. In Press.
[68]Luna Majumder, C.S. Manohar. A time-domain approach for damage detection in beam structures using vibration data with a moving oscillator as an excitation source. Journal of Sound and Vibration,2003,268:699-716.
[69]Luna Majumder, C.S. Manohar. Nonlinear reduced models for beam damage detection using data on moving oscillator-beam interactions. Computers and Structures,2004,82: 301-314.
[70]卜建清,王树栋,罗韶湘.由车激响应识别桥梁损伤的灵敏度方法.振动与冲击,2007,26(7)：80-86.
[71]王树栋,卜建清,娄国充.基于过桥汽车动力响应的桥梁损伤识别.长安大学学报(自然科学版),2008,28(3)：45-50.
[72]李杰,陈隽.结构参数未知条件下的地震动反演研究.地震工程与工程振动,1997,17(3)：27-35.
[73]李杰,陈隽.子结构物理参数识别与输入地震动的复合反演研究.振动与冲击,1998,17(1)：58-53.
[74]Duan Wang, Achintya Haldar. Element level system Identification with unknown input. Journal of Engineering Mechanics,1994,120(1):159-176.
[75]Duan Wang, Achintya Haldar. System identification with limited observation and without input. Journal of Engineering Mechanics,1997,123(5):504-511.
[76]陈隽,李杰.高层建筑风荷载反演研究.力学季刊,2001.,22(1)：72-77.
[77]李杰,陈隽.未知输入条件下的结构物理参数识别研究.计算力学学报.1999,16(1)：32-41.
[78]X.Y. Li, S.S. Law. Structural damage detection with statistical analysis from support excitation. Mechanical Systems and Signal Processing,2008,22:1793-1808.
[79]Michael Link. Matthias Weiland. Damage identification by multi-model updating in the modal and in the time domain. Mechanical Systems and Signal Processing,2009,23: 1734-1746。
[80]陈隽,李杰.部分输入未知条件下的结构系统识别研究.地震工程与工程振动,1998,18(4)：40-47.
[81]陈隽,李杰.系统非线性参数识别的松弛法.振动工程学报,2001,14(4)：447-450.
[82]李杰,陈隽.部分输入未知时求解动力复合反演问题的补偿算法.计算力学学报,2002,19(3)：310-314.
[83]K. D. Hjelmstad, Mo. R. Banan, Ma. R. Banan. Time-domain parameter estimation algorithm for structures I:Computation aspects. Journal of engineering mechanics,1995, 121(3):424-434.
[84]Mo. R. Banan, Ma. R. Banan, K. D. Hjelmstad. Time-domain parameter estimation algorithm for structures II:Numerical simulation studies. Journal of engineering mechanics, 1995,121(3):435-447.
[85]安鸿志.时间序列分析.上海：华东师范大学出版社,1992.
[86]K. Krishnan Nair, Anne S. Kiremidjian, Kincho H. Law. Time series-based damage detection and localization algorithm with application to the ASCE benchmark structure. Journal of Sound and Vibration,2006,291:349-368.
[87]H Zheng, A Mita. Two-stage damage diagnosis based on the distance between ARMA models and pre-whitening filters. Smart Materials and Structures,2007,16:1829-1836.
[88]Yong Lu, Feng Gao. A novel time-domain auto-regressive model for structural damage diagnosis. Journal of Sound and Vibration,2005,283:1031-1049.
[89]Hoon Sohn, Charles R Farrar. Damage diagnosis using time series analysis of vibration signals. Smart Material Structures.2001,10:446-451.
[90]Hoon Sohn, Charles R. Farrar. Time Series Analyses for Locating Damage Sources in Vibration Systems. International Conference on Noise and Vibration Engineering, Leuven, Belgium,2000:13-15.
[91]陈志为,林有勤,任伟新.用AR模型判断结构损伤的方法.福州大学学报(自然科学版).2005,33(增刊),301-304.
[92]冉启文.小波分析方法及其应用.哈尔滨：哈尔滨工业大学出版社,1995.
[93]W.L. Bayissa, N. Haritos, S. Thelandersson. Vibration-based structural damage identification using wavelet transform. Mechanical Systems and Signal Processing,2008,22: 1194-1215.
[94]Akira Sone, Shizuo Yamamoto, Akira Nakaoka, et al. Health Monitoring System of Structures Based on Orthonormal Wavelet Transform. Seismic Engineering. Transactions of ASME.1995,321:161-167.
[95]Ryutaro Segawa, Akira Sone, Arata Masuda. System Identification of MDOF Structures by Wavelet Transform. Proceedings of the U.S.-Japan Joint Workshop and Third Grantees Meeting, University of Washington, Seattle, Washington,2001:15-16.
[96]Adriana Hera, Zhikun Hou. Application of Wavelet Approach for ASCE Structural Health Monitoring Benchmark Studies. Journal of engineering mechanics,2004, 126(7):677-683.
[97]Jian-Gang Han, Wei-Xin Ren, Zeng-Shou Sun. Wavelet packet based damage identification of beam structures. International Journal of Solids and Structures,2005,42: 6610-6627.
[98]Y.J. Yan, L.H. Yam. Online detection of crack damage in composite plates using embedded piezoelectric actuators/sensors and wavelet analysis. Composite Structures,2002, 58:29-38.
[99]Wei-Xin Ren, Zeng-Shou Sun. Structural damage identification by using wavelet entropy. Engineering Structures,2008,30:2840-2849.
[100]丁幼亮,李爱群.基于小波包分析的Benchmark结构损伤预警试验研究.工程力学,2008,25(11)：128-134.
[101]丁幼亮,李爱群,缪长青.环境激励下基于小波包分析的结构损伤预警方法.应用力学学报,2008,25(3)：366-370.
[102]何浩祥,孙立,闫维明,等.基于小波分析的结构损伤信号奇异性检测.工业建筑,2007,37(增刊)：204-207.
[103]Norden E. Huang, Zheng Shen, Steven R. Long, et al. The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis. in:Wu M C and Shih H H. Proceedings of the Royal society of London-Series A. London: The Royal Society,1998,903-995.
[104]Jann N. Yang, Ying Lei, Shuwen Pan, et al. Hilbert-Huang Based Approach for Structural Damage Detection. Journal of engineering mechanics,2004,130(1):85-95.
[105]Jann N. Yang, Ying Lei, Shuwen Pan, et al. System identification of linear structures based on Hilbert-Huang spectral analysis. Part 1:normal modes. Earthquake engineering and structural dynamics,2003,32:1443-1467.
[106]Jann N. Yang, Ying Lei, Shuwen Pan, et al. System identification of linear structures based on Hilbert-Huang spectral analysis. Part 2:Complex modes. Earthquake engineering and structural dynamics,2003,32:1533-1554.
[107]Y. L. Xu, M.ASCE, J. Chen. Structural Damage Detection Using Empirical Mode Decomposition:Experimental Investigation. Journal of engineering mechanics,2004, 130(11):1279-1288.
[108]Helong Li, Xiaoyan Deng, Hongliang Dai. Structural damage detection using the combination method of EMD and wavelet analysis. Mechanical Systems and Signal Processing,2007,21:298-306.
[109]Dong Yinfeng, Li Yingmin, Lai Ming. Structural damage detection using empirical-mode decomposition and vector autoregressive moving average model. Soil Dynamics and Earthquake Engineering,2010,30:133-145.
[110]李传习,陈富强.基于HHT的结构损伤特征量与异常诊断.长沙理工大学学报(自然科学版),2007,4(3)：29-33.
[111]屈文忠,陈明祥,曾又林.利用HHT方法进行结构损伤检测研究.武汉大学学报(工学版),2007,40(5)：77-81.
[112]程磊,瞿伟廉.基于Hilbert-Huang变换理论的结构损伤检测.防灾减灾工程学报,2007,27(3)：318-322.
[113]Ugo Galvanetto, George Violaris. Numerical investigation of a new damage detection method based on proper orthogonal decomposition. Mechanical Systems and Signal Processing,2007,21:1346-1361.
[114]Zhao-Dong Xu, Zhishen Wu. Energy damage detection strategy based on acceleration responses for long-span bridge structures. Engineering Structures,2007,29: 609-617.
[115]黄方林,顾松年.用残余能量法诊断结构故障.机械强度,1996,18(4)：5-9.
[116]边肇祺,张学工.模式识别.第二版.北京：清华大学出版社,2000.
[117]Vladimir N. Vapnik,著,许建华,张学工,译.统计学习理论.北京：电子工业出版社,2009.
[118]Charles R. Farrar, Thomas A. Duffey, Scott W. Doebling, et al. A Statistical Pattern Recognition Paradigm for Vibration-Based Structural Health Monitoring. The 2nd International Workshop on Structural Health Monitoring, Stanford, CA,1999.
[119]Hoon Sohn, Charles R. Farrar, Norman F. Hunter, et al. Structural Health Monitoring Using Statistical Pattern Recognition Techniques. Journal of Dynamics Systems, Measurement and Control,2001,123(4):706-711.
[120]Hoon Sohn, Charles R. Farrar. Statistical Process Control and Projection Techniques for Structural health monitoring. European COST F3 Conference on System Identification and Structural Health Monitoring, Madrid, Spain,2000.
[121]Hoon Sohn, Jerry A. Czarnecki, Charles R. Farrar, et al. Structural health monitoring using statistical process control. Journal of Structural Engineering,2000, 126(11):1356-1363.
[122]K. Worden, G. Manson, N. R. J. Fieller. Damage detection using outlier analysis. J. Sound and Vibration,2000,229(3):647-667.
[123]Mustafa Gul, F. Necati Catbas. Statistical pattern recognition for Structural Health Monitoring using time series modeling:Theory and experimental verifications, Mechanical Systems and Signal Processing,2009,23:2192-2204.
[124]Zengrong Wang, K. C. G. Ong. Multivariate Statistical Approach to Structural Damage Detection. Journal of engineering mechanics,2010,136:12-22.
[125]Q.W. Zhang. Statistical damage identification for bridges using ambient vibration data. Computers and Structures,2007,85:476-485.
[126]张启伟.桥梁健康监测中的损伤特征提取与异常诊断.同济大学学报,2003,31(3)：258-262.
[127]邱洪兴,蒋永生.古塔结构损伤的系统识别Ⅰ：理论.东南大学学报(自然科学版).2001,31(2)：81-85.
[128]邱洪兴,蒋永生.古塔结构损伤的系统识别Ⅱ：应用.东南大学学报(自然科学版).2001,31(2)：86-90.
[129]Vladimir N.Vapnik,著,张学工,译.统计学习理论的本质.北京：清华大学出版社,2000.
[130]Oliver R. de Lautour, Piotr Omenzetter. Damage classification and estimation in experimental structures using time series analysis and pattern recognition. Mechanical Systems and Signal Processing,2010,24:1556-1569.
[131]Xiaomo Jiang, Hojjat Adeli. Pseudospectra, MUSIC, and dynamic wavelet neural network for damage detection of highrise buildings. International journal for numerical methods in engineering,2007;71:606-629.
[132]Mitsuru Nakamura, Sami F. Masri, Anastassios G. Chassiakos, et al. A method for non-parametric damage detection through the use of neural networks. Earthquake engineering and structural dynamics,1998,27:997-1010.
[133]S. F. Masri, A. W. Smyth, A. G. Chassiakos, et al. Application of neural networks for detection of changes in nonlinear systems. Journal of engineering mechanics,2000, 126(7):666-676.
[134]许斌,卢平,宋钢兵.基于加速度时程的结构参数直接识别方法及验证.工程力学,26(9)：87-93.
[135]章莉莉,许斌,宋钢兵.基于位移时程的框架模型结构参数与损伤识别.华中科技大学学报(城市科学版),2008,25(3)：184-187.
[136]C.Y. Kao, Shih-Lin Hung. Detection of structural damage via free vibration responses generated by approximating artificial neural networks. Computers and Structures, 2003,81:2631-2644.
[137]Chang Kook Oh, HoonSohn. Damage diagnosis under environmental and operational variations using unsupervised support vector machine. Journal of Sound and Vibration,2009,325:224-239.
[138]Luke Bornn, Charles R. Farrar, Gyuhae Park, et al. Structural Health Monitoring With Autoregressive Support Vector Machines. Journal of Vibration and Acoustics,2009, 131:1-9.
[139]Ahmet Bulut, Ambuj K. Singh, Peter Shin, et al. Real-time Nondestructive Structural Health Monitoring using Support Vector Machines and Wavelets. Advanced Sensor Technologies for Nondestructive Evaluation and Structural Health Monitoring, San Diego, USA,2005.
[140]王凯,张永祥,李军.基于支持向量机的齿轮故障诊断方法研究.振动与冲击,2006,25(6)：97-101.
[141]何浩祥,闫维明,彭凌云.基于支持向量机的钢筋混凝土桥梁损伤识别.公路交通科技,2008,25(3)：65-70.
[142]何浩祥,闫维明,周锡元.小波支持向量机在结构损伤识别中的应用研究.振动、测试与诊断,2007,27(1)：53-58.
[143]赵学风,段晨东,刘义艳.基于小波包变换的支持向量机损伤诊断方法.振动、测试与诊断,2008,28(2)：104-108.
[144]陈理渊,黄进.基于支持向量机的电机转子断条故障诊断.电工技术学报,2006,21(8)：48-52.
[145]S.S. Law, X.Q. Zhu. Dynamic behavior of damaged concrete bridge structures under moving vehicular loads. Engineering Structures,2004,26:1279-1293.
[146]A. Ariaei, S. Ziaei-Rad, M. Ghayour. Vibration analysis of beams with open and breathing cracks subjected to moving masses. Journal of Sound and Vibration,2009,326: 709-724.
[147]Hai-Ping Lin, Shun-Chang Chang. Forced responses of cracked cantilever beams subjected to a concentrated moving load. International Journal of Mechanical Sciences,2006, 48:1456-1463.
[148]C. Bilelloa, L.A. Bergman. Vibration of damaged beams under a moving mass: theory and experimental validation. Journal of Sound and Vibration,2004,274:567-582.
[149]S.A. Neild, M.S. Williams, P.D. McFadden. Non-linear behavior of reinforced concrete beams under low amplitude cyclic and vibration loads. Engineering Structures, 2002,24:707-718.
[150]Achmad Widodo, Bo-Suk Yang. Support vector machine in machine condition monitoring and fault diagnosis. Mechanical Systems and Signal Processing,2007,21: 2560-2574.
[151]方瑞明.支持向量机理论及其应用分析.北京：中国电力出版社,2007.
[152]高晋占.微弱信号检测.北京：清华大学出版社,2004.
[153]邹伟.大跨度连续刚构桥易损性研究.成都：西南交通大学,2009
[154]P. Cawley, r. D. Adams.Location of defects in structures from measurements of natural frequencies. Journal of strain analysis,1979,14(2):49-57.
[155]H. F. Lam, J. M. Ko, C. W. Wong. Localization of damaged structural connections based on experimental modal and sensitivity and analysis. Journal of sound and vibration, 1998,200(1):91-115.
[156]Sarunas J. Raudys, Anil K. Jain. Small Sample Size Effects in Statistical Pattern Recognition:Recommendations for Practitioners, IEEE Transactions on Pattern Analysis and Machine Intelligence.1991,13(3):252-264.
[157]Thomas M. Cover. Geometrical and Statistical Properties of Systems of Linear Inequalities with Applications in Pattern Recognition, IEEE Transactions on electronic computers.1965,EC-14:326-334.
[158]姜文瀚.模式识别中的样本选择研究及其应用.南京：南京理工大学博士研究生学位论文.2007.
[159]陈伟,王松文.25型客车及其新技术.北京：中国铁道出版社,2001.
[160]宋顺宝.客车车辆构造与检修.北京：中国铁道出版社,2003.
[161]夏禾.车辆与结构动力相互作用.北京：科学出版社,2002.
[162]屈文俊,车惠民.既有铁路混凝土桥梁的病害分析.桥梁建设,1995,4：64-68.
[163]付春雨,李乔.考虑空间整体效应的温度自应力计算.铁道学报,2007,29(6)：80-83.
[164]付春雨,李乔.平曲线箱梁在温差作用下的扭转分析.西南交通大学学报,2009,44(5)：710-715.
[165]姜静清.最小二乘支持向量机算法及应用研究.长春：吉林大学博士研究生学位论文.2007.
[166]邓乃扬,田英杰.数据挖掘中的新方法——支持向量机.北京：科学出版社,2004.
[2]黄方林,王学敏,陈政清,等.大型桥梁健康监测研究进展.中国铁道科学,2005,26(2)：1-7.
[3]Darryll Pines, A Emin Aktan. Status of structural health monitoring of long-span bridges in the United States. Progress in Structural Engineering and Materials,2002,4: 372-380.
[4]Charles R Farrar, David A Jauregui. Comparative study of damage identification algorithms applied to a bridge:Ⅰ. Experiment. Smart Materials and Structures,1998, 7(5):704-719.
[5]Charles R Farrar, David A Jauregui. Comparative study of damage identification algorithms applied to a bridge:Ⅱ. Numerical study. Smart Materials and Structures, 1998,7(5):720-731.
[6]Catbas F. Necati, Aktan A Emin. Condition and damage assessment:issues and some promising indices. Journal of structural engineering,2002,128(8):1026-1036.
[7]Catbas F. Necati, Melih Susoy, Dan M. Frangopol. Structural health monitoring and reliability estimation:Long span truss bridge application with environmental monitoring data. Engineering Structures,2008,30:2347-2359.
[8]Bart Peeters, Guido De Roeck. One-year monitoring of the Z24-Bridge:environmental effects versus damage events. Earthquake Engineering & Structural Dynamics,2001, 30:149-171.
[9]Bart Peeters, Johan Maeck, Guido De Roeck. Dynamic monitoring of the z24-bridge: separating temperature effects from damage. In proceedings of the European COST F3 Conference on System Identification and Structural Health monitoring, Madrid, Spain, June 2000.377-386.
[10]C. Kramer, C. A. M. de Smet, G. de Roeck. Z24 Bridge Damage Detection Tests. Proceedings of the 1999 17th International Modal Analysis Conference, Orlando FL, 1999:1023-1029.
[11]J.B. Frandsen. Simultaneous pressures and accelerations measured full-scale on the Great Belt East suspension bridge. Journal of Wind Engineering and Industrial Aerodynamics,2001,89:95-129.
[12]Yozo Fujino. Vibration, control and monitoring of long-span bridges—recent research, developments and practice in Japan. Journal of Constructional Steel Research,2002, 58:71-97.
[13]Jinping Ou, H.M. Koh, S. Kim, Wolfgang Habel, et al. Sensing Issues in Civil Structural Health Monitoring. Springer Netherlands,2005.
[14]Akira Mita. Emerging needs in Japan for health monitoring technologies in civil and building structures.2nd International Workshop on Structural Health Monitoring, USA, Stanford University, Sept,1999.1-12.
[15]Chung-Bang Yun, Jong-Jae Lee, Sung-Kon Kim, et al. Recent R&D Activities on Structural Health Monitoring for Civil Infra-Structures in Korea. KSCE Journal of Civil Engineering,2003,7(6):637-651.
[16]Sung-Kon Kim. Intelligent Health Monitoring System for a Cable-Stayed Bridge. KSCE Journal of Civil Engineering,2001,5:37-41.
[17]李宏男,高东伟,伊廷华.土木工程结构健康监测系统的研究状况与进展.力学进展,2008,38(2)：151-166.
[18]朱桂新,陈旭东,王迎军GPS RTK技术在虎门大桥运营安全监测中的应用.公路,2002,7：55-58.
[19]邱法维,杜文博,钱稼茹.虎门大桥应变监测数据处理系统设计.桥梁建设,2003,2：66-69.
[20]缪长青,李爱群,冯兆祥等.润扬大桥结构健康监测系统设计研究.世界桥梁,2006,3：63-66.
[21]李爱群,丁幼亮.工程结构损伤预警理论及其应用.北京：科学出版社,2007.
[22]张敏,杨志芳,朱利明等.东海大桥桥梁结构健康监测系统研究与设计.桥梁建设,2006,2：67-70.
[23]牛进民,吕令红,李金仙等.东明黄河公路大桥健康与安全监测的研究及应用.华东公路,2009,2：80-83.
[24]朱绍玮,张宇峰,樊可清等.江阴大桥结构健康监测系统升级改造及初步数据分析.公路,2007,1：69-73.
[25]何旭辉,陈政清,黄方林.南京长江大桥结构健康监测及其关键技术研究.中国铁道科学,2008,29(4)：138-140.
[26]余波,邱洪兴,王浩等.苏通大桥结构健康监测系统设计.地震工程与工程振动.2009,29(4)：170-177.
[27]苏木标,杜彦良,孙宝臣等.芜湖长江大桥长期健康监测与报警系统研究.铁道学报,2007,29(2)：71-76.
[28]宗周红,孙建林,徐立群等.下白石大桥健康监测系统的设计与研究.铁道学报,2008,30(5)：65-71.
[29]曹映泓Ming L. Wang,朱利明.湛江海湾大桥分布式健康监测系统开发研究.公路,2007,1：56-62.
[30]王俊如,阵伟民,刘纲等.重庆石板坡长江大桥复线桥的受力分析及健康监测系统设计.现代交通技术,2008,5(5)：37-40.
[31]单德山,李乔,付春雨,严鹏.智能桥梁健康监测与损伤评估.北京：人民交通出版社,2010.
[32]刘正光,黄钊猷.香港缆索桥结构健康监测系统.市政技术,2005,23(z1)：22-25.
[33]李惠彬,秦权,钱良忠.青马悬索桥的时域模态识别.土木工程学报,2001,34(5)：52-57.
[34]王柏生,倪一清,高赞明.青马大桥桥板结构损伤位置识别的数值模拟.土木工程学报,34(3)：67-73.
[35]Olof G. Gustafsson; Tibor Tallian. Detection of Damage in Assembled Rolling Element Bearings. Tribology Transactions,1962,5(1):197-209.
[36]杨智春,于哲峰.结构健康监测中的损伤检测技术研究进展.力学进展,2004,34(2)：215-223.
[37]Shih-Chi Liu, James T.P. Yao. Structural Identification Concept. Journal of the Structural Division,1978,104(12):1848-1858.
[38]谢献忠,易伟建,王修勇,等.结构损伤诊断与系统时域辨识研究综述.中国安全科学学报,2008,18(6)：110-115.
[39]杨秋伟,基于振动的结构损伤识别方法研究进展.振动和冲击,2007,26(10)：86-91.
[40]Scott W. Doebling, Charles R. Farrar, Michael B. Prime. A summary review of vibration-based damage identification methods. The Shock and vibration digest,1998, 30:91-105.
[41]D. Montalvao, N.M.M. Maia, A.M.R. Ribeiro. A Review of Vibration-based Structural Health Monitoring with Special Emphasis on Composite Materials. The Shock and vibration digest,2006,38:295-324.
[42]Spilios D Fassois, John S Sakellariou. Time-series methods for fault detection and identification in vibrating structures. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences,2007,365:411-448.
[43]Banan M R, Hjelmstad K D. Parameter Estimation of Structures from Static response I:Computational Aspects. Journal of Structural Engineering, ASCE,1994,120 (11): 3243-3258.
[44]Banan M R, Hjelmstad K D. Parameter Estimation of Structures from Static Response Ⅱ:Numerical Simulation Studies[J].Journal of Structural Engineering, ASCE,1994, 120(11):3259-3283.
[45]崔飞,杨党旗,高岩.用参数识别技术进行桥梁结构损伤识别.中国铁道科学,2004,24(3)：36.
[46]向天宇,赵人达,刘海波.基于静力测试数据的预应力混凝土连续梁结构损伤识别. 土木工程学报,2003,36(11)：79-82.
[47]付春雨,李乔,单德山.基于位移连续的静力损伤识别,桥梁建设,2010,2：18-20.
[48]Gaetan Kerschen, Keith Worden, Alexander F. Vakakis, et al. Past, present and future of nonlinear system identification in structural dynamics. Mechanical Systems and Signal Processing,2006,20:505-592.
[49]Y.J. Yan, L. Cheng, Z.Y. Wu. et al. Development in vibration-based structural damage detection technique. Mechanical Systems and Signal Processing,2007,21:2198-2211.
[50]秦仙蓉,张令弥,顾明等.改进的基于模态参数的结构计算模型修正算法.同济大学学报,2002,30(11)：1295-1999.
[51]张刚刚,徐岳.桥梁结构损伤识别的动力指纹分析方法[J].中外公路.2005,25(2)：61-63.
[52]Hoon Sohn, Charles R. Farrar. Statistical Process Control and Projection Techniques for Damage Detection. European COST F3 Conference on System Identification and Structural Health Monitoring, Madrid, Spain,2000,105-114.
[53]姜增国,张桢.结构损伤检测类MAC应变频响函数识别指标的研究.交通科技,2006,1,7-9.
[54]冉志红.桥梁结构损伤识别的动力指纹方法研究.成都：西南交通大学博士研究生学位论文.2007.
[55]张育智.基于神经网络与数据融合的结构损伤识别理论研究.成都：西南交通大学博士研究生学位论文.2007.
[56]陈晓强.基于能量指标的结构损伤识别理论与试验研究.武汉：华中科技大学博士研究生学位论文.2009.
[57]西南交通大学土木工程学院.铁道部科技研究开发计划重大课题“智能化桥梁结构研究”结题报告-损伤识别分册.西南交通大学.2009.
[58]李国强,李杰.工程结构动力检测与应用.科学出版社,2002.
[59]Peter Van Overschee, Bart De Moor. Subspace algorithms for the stochastic identification problem. Proceedings of the 30th Conference on Decision and Control, Brighton, England,1991,1321-1326.
[60]S. Alampalli. Effects of testing, analysis, damage, and environment on modal parameters. Mechanical Systems and Signal Processing,2000,14(1):63-74.
[61]Hoon Sohn. Effects of environmental and operational variability on structural health monitoring, Philosophical Transactions of the Royal Society,2007,365:539-560.
[62]H. Sohn, M. Dzwonczyk, E.G. Straser, et al. An experimental study of temperature effect on modal parameters of the Alamosa Canyon Bridge. Earthquake Engineering & Structural Dynamics,1999,28:879-897.
[63]谢献忠,易伟建,刘锡军等.非线性时域识别方程的不适定性与正则化方法研究. 振动与冲击.2006,25(5)：120-124.
[64]X. Q. Zhu, S. S. Law. Moving Loads Identification through Regularization. Journal of engineering mechanics,2002,128(9):989-1000.
[65]X. Q. Zhu, S. S. Law. Damage Detection in Simply Supported Concrete Bridge Structure under Moving Vehicular Loads. Journal of vibration and acoustics.2007, 129(1):58-63.
[66]Z.R. Lu, S.S. Law. Identification of system parameters and input force from output only. Mechanical Systems and Signal Processing,2007,21:2099-2111.
[67]Z.R. Lu, S. S. Law. Differentiating damage effects in a structural component from the time response. Mechanical Systems and Signal Processing, Available online 28 April 2010. In Press.
[68]Luna Majumder, C.S. Manohar. A time-domain approach for damage detection in beam structures using vibration data with a moving oscillator as an excitation source. Journal of Sound and Vibration,2003,268:699-716.
[69]Luna Majumder, C.S. Manohar. Nonlinear reduced models for beam damage detection using data on moving oscillator-beam interactions. Computers and Structures,2004,82: 301-314.
[70]卜建清,王树栋,罗韶湘.由车激响应识别桥梁损伤的灵敏度方法.振动与冲击,2007,26(7)：80-86.
[71]王树栋,卜建清,娄国充.基于过桥汽车动力响应的桥梁损伤识别.长安大学学报(自然科学版),2008,28(3)：45-50.
[72]李杰,陈隽.结构参数未知条件下的地震动反演研究.地震工程与工程振动,1997,17(3)：27-35.
[73]李杰,陈隽.子结构物理参数识别与输入地震动的复合反演研究.振动与冲击,1998,17(1)：58-53.
[74]Duan Wang, Achintya Haldar. Element level system Identification with unknown input. Journal of Engineering Mechanics,1994,120(1):159-176.
[75]Duan Wang, Achintya Haldar. System identification with limited observation and without input. Journal of Engineering Mechanics,1997,123(5):504-511.
[76]陈隽,李杰.高层建筑风荷载反演研究.力学季刊,2001.,22(1)：72-77.
[77]李杰,陈隽.未知输入条件下的结构物理参数识别研究.计算力学学报.1999,16(1)：32-41.
[78]X.Y. Li, S.S. Law. Structural damage detection with statistical analysis from support excitation. Mechanical Systems and Signal Processing,2008,22:1793-1808.
[79]Michael Link. Matthias Weiland. Damage identification by multi-model updating in the modal and in the time domain. Mechanical Systems and Signal Processing,2009,23: 1734-1746。
[80]陈隽,李杰.部分输入未知条件下的结构系统识别研究.地震工程与工程振动,1998,18(4)：40-47.
[81]陈隽,李杰.系统非线性参数识别的松弛法.振动工程学报,2001,14(4)：447-450.
[82]李杰,陈隽.部分输入未知时求解动力复合反演问题的补偿算法.计算力学学报,2002,19(3)：310-314.
[83]K. D. Hjelmstad, Mo. R. Banan, Ma. R. Banan. Time-domain parameter estimation algorithm for structures I:Computation aspects. Journal of engineering mechanics,1995, 121(3):424-434.
[84]Mo. R. Banan, Ma. R. Banan, K. D. Hjelmstad. Time-domain parameter estimation algorithm for structures II:Numerical simulation studies. Journal of engineering mechanics, 1995,121(3):435-447.
[85]安鸿志.时间序列分析.上海：华东师范大学出版社,1992.
[86]K. Krishnan Nair, Anne S. Kiremidjian, Kincho H. Law. Time series-based damage detection and localization algorithm with application to the ASCE benchmark structure. Journal of Sound and Vibration,2006,291:349-368.
[87]H Zheng, A Mita. Two-stage damage diagnosis based on the distance between ARMA models and pre-whitening filters. Smart Materials and Structures,2007,16:1829-1836.
[88]Yong Lu, Feng Gao. A novel time-domain auto-regressive model for structural damage diagnosis. Journal of Sound and Vibration,2005,283:1031-1049.
[89]Hoon Sohn, Charles R Farrar. Damage diagnosis using time series analysis of vibration signals. Smart Material Structures.2001,10:446-451.
[90]Hoon Sohn, Charles R. Farrar. Time Series Analyses for Locating Damage Sources in Vibration Systems. International Conference on Noise and Vibration Engineering, Leuven, Belgium,2000:13-15.
[91]陈志为,林有勤,任伟新.用AR模型判断结构损伤的方法.福州大学学报(自然科学版).2005,33(增刊),301-304.
[92]冉启文.小波分析方法及其应用.哈尔滨：哈尔滨工业大学出版社,1995.
[93]W.L. Bayissa, N. Haritos, S. Thelandersson. Vibration-based structural damage identification using wavelet transform. Mechanical Systems and Signal Processing,2008,22: 1194-1215.
[94]Akira Sone, Shizuo Yamamoto, Akira Nakaoka, et al. Health Monitoring System of Structures Based on Orthonormal Wavelet Transform. Seismic Engineering. Transactions of ASME.1995,321:161-167.
[95]Ryutaro Segawa, Akira Sone, Arata Masuda. System Identification of MDOF Structures by Wavelet Transform. Proceedings of the U.S.-Japan Joint Workshop and Third Grantees Meeting, University of Washington, Seattle, Washington,2001:15-16.
[96]Adriana Hera, Zhikun Hou. Application of Wavelet Approach for ASCE Structural Health Monitoring Benchmark Studies. Journal of engineering mechanics,2004, 126(7):677-683.
[97]Jian-Gang Han, Wei-Xin Ren, Zeng-Shou Sun. Wavelet packet based damage identification of beam structures. International Journal of Solids and Structures,2005,42: 6610-6627.
[98]Y.J. Yan, L.H. Yam. Online detection of crack damage in composite plates using embedded piezoelectric actuators/sensors and wavelet analysis. Composite Structures,2002, 58:29-38.
[99]Wei-Xin Ren, Zeng-Shou Sun. Structural damage identification by using wavelet entropy. Engineering Structures,2008,30:2840-2849.
[100]丁幼亮,李爱群.基于小波包分析的Benchmark结构损伤预警试验研究.工程力学,2008,25(11)：128-134.
[101]丁幼亮,李爱群,缪长青.环境激励下基于小波包分析的结构损伤预警方法.应用力学学报,2008,25(3)：366-370.
[102]何浩祥,孙立,闫维明,等.基于小波分析的结构损伤信号奇异性检测.工业建筑,2007,37(增刊)：204-207.
[103]Norden E. Huang, Zheng Shen, Steven R. Long, et al. The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis. in:Wu M C and Shih H H. Proceedings of the Royal society of London-Series A. London: The Royal Society,1998,903-995.
[104]Jann N. Yang, Ying Lei, Shuwen Pan, et al. Hilbert-Huang Based Approach for Structural Damage Detection. Journal of engineering mechanics,2004,130(1):85-95.
[105]Jann N. Yang, Ying Lei, Shuwen Pan, et al. System identification of linear structures based on Hilbert-Huang spectral analysis. Part 1:normal modes. Earthquake engineering and structural dynamics,2003,32:1443-1467.
[106]Jann N. Yang, Ying Lei, Shuwen Pan, et al. System identification of linear structures based on Hilbert-Huang spectral analysis. Part 2:Complex modes. Earthquake engineering and structural dynamics,2003,32:1533-1554.
[107]Y. L. Xu, M.ASCE, J. Chen. Structural Damage Detection Using Empirical Mode Decomposition:Experimental Investigation. Journal of engineering mechanics,2004, 130(11):1279-1288.
[108]Helong Li, Xiaoyan Deng, Hongliang Dai. Structural damage detection using the combination method of EMD and wavelet analysis. Mechanical Systems and Signal Processing,2007,21:298-306.
[109]Dong Yinfeng, Li Yingmin, Lai Ming. Structural damage detection using empirical-mode decomposition and vector autoregressive moving average model. Soil Dynamics and Earthquake Engineering,2010,30:133-145.
[110]李传习,陈富强.基于HHT的结构损伤特征量与异常诊断.长沙理工大学学报(自然科学版),2007,4(3)：29-33.
[111]屈文忠,陈明祥,曾又林.利用HHT方法进行结构损伤检测研究.武汉大学学报(工学版),2007,40(5)：77-81.
[112]程磊,瞿伟廉.基于Hilbert-Huang变换理论的结构损伤检测.防灾减灾工程学报,2007,27(3)：318-322.
[113]Ugo Galvanetto, George Violaris. Numerical investigation of a new damage detection method based on proper orthogonal decomposition. Mechanical Systems and Signal Processing,2007,21:1346-1361.
[114]Zhao-Dong Xu, Zhishen Wu. Energy damage detection strategy based on acceleration responses for long-span bridge structures. Engineering Structures,2007,29: 609-617.
[115]黄方林,顾松年.用残余能量法诊断结构故障.机械强度,1996,18(4)：5-9.
[116]边肇祺,张学工.模式识别.第二版.北京：清华大学出版社,2000.
[117]Vladimir N. Vapnik,著,许建华,张学工,译.统计学习理论.北京：电子工业出版社,2009.
[118]Charles R. Farrar, Thomas A. Duffey, Scott W. Doebling, et al. A Statistical Pattern Recognition Paradigm for Vibration-Based Structural Health Monitoring. The 2nd International Workshop on Structural Health Monitoring, Stanford, CA,1999.
[119]Hoon Sohn, Charles R. Farrar, Norman F. Hunter, et al. Structural Health Monitoring Using Statistical Pattern Recognition Techniques. Journal of Dynamics Systems, Measurement and Control,2001,123(4):706-711.
[120]Hoon Sohn, Charles R. Farrar. Statistical Process Control and Projection Techniques for Structural health monitoring. European COST F3 Conference on System Identification and Structural Health Monitoring, Madrid, Spain,2000.
[121]Hoon Sohn, Jerry A. Czarnecki, Charles R. Farrar, et al. Structural health monitoring using statistical process control. Journal of Structural Engineering,2000, 126(11):1356-1363.
[122]K. Worden, G. Manson, N. R. J. Fieller. Damage detection using outlier analysis. J. Sound and Vibration,2000,229(3):647-667.
[123]Mustafa Gul, F. Necati Catbas. Statistical pattern recognition for Structural Health Monitoring using time series modeling:Theory and experimental verifications, Mechanical Systems and Signal Processing,2009,23:2192-2204.
[124]Zengrong Wang, K. C. G. Ong. Multivariate Statistical Approach to Structural Damage Detection. Journal of engineering mechanics,2010,136:12-22.
[125]Q.W. Zhang. Statistical damage identification for bridges using ambient vibration data. Computers and Structures,2007,85:476-485.
[126]张启伟.桥梁健康监测中的损伤特征提取与异常诊断.同济大学学报,2003,31(3)：258-262.
[127]邱洪兴,蒋永生.古塔结构损伤的系统识别Ⅰ：理论.东南大学学报(自然科学版).2001,31(2)：81-85.
[128]邱洪兴,蒋永生.古塔结构损伤的系统识别Ⅱ：应用.东南大学学报(自然科学版).2001,31(2)：86-90.
[129]Vladimir N.Vapnik,著,张学工,译.统计学习理论的本质.北京：清华大学出版社,2000.
[130]Oliver R. de Lautour, Piotr Omenzetter. Damage classification and estimation in experimental structures using time series analysis and pattern recognition. Mechanical Systems and Signal Processing,2010,24:1556-1569.
[131]Xiaomo Jiang, Hojjat Adeli. Pseudospectra, MUSIC, and dynamic wavelet neural network for damage detection of highrise buildings. International journal for numerical methods in engineering,2007;71:606-629.
[132]Mitsuru Nakamura, Sami F. Masri, Anastassios G. Chassiakos, et al. A method for non-parametric damage detection through the use of neural networks. Earthquake engineering and structural dynamics,1998,27:997-1010.
[133]S. F. Masri, A. W. Smyth, A. G. Chassiakos, et al. Application of neural networks for detection of changes in nonlinear systems. Journal of engineering mechanics,2000, 126(7):666-676.
[134]许斌,卢平,宋钢兵.基于加速度时程的结构参数直接识别方法及验证.工程力学,26(9)：87-93.
[135]章莉莉,许斌,宋钢兵.基于位移时程的框架模型结构参数与损伤识别.华中科技大学学报(城市科学版),2008,25(3)：184-187.
[136]C.Y. Kao, Shih-Lin Hung. Detection of structural damage via free vibration responses generated by approximating artificial neural networks. Computers and Structures, 2003,81:2631-2644.
[137]Chang Kook Oh, HoonSohn. Damage diagnosis under environmental and operational variations using unsupervised support vector machine. Journal of Sound and Vibration,2009,325:224-239.
[138]Luke Bornn, Charles R. Farrar, Gyuhae Park, et al. Structural Health Monitoring With Autoregressive Support Vector Machines. Journal of Vibration and Acoustics,2009, 131:1-9.
[139]Ahmet Bulut, Ambuj K. Singh, Peter Shin, et al. Real-time Nondestructive Structural Health Monitoring using Support Vector Machines and Wavelets. Advanced Sensor Technologies for Nondestructive Evaluation and Structural Health Monitoring, San Diego, USA,2005.
[140]王凯,张永祥,李军.基于支持向量机的齿轮故障诊断方法研究.振动与冲击,2006,25(6)：97-101.
[141]何浩祥,闫维明,彭凌云.基于支持向量机的钢筋混凝土桥梁损伤识别.公路交通科技,2008,25(3)：65-70.
[142]何浩祥,闫维明,周锡元.小波支持向量机在结构损伤识别中的应用研究.振动、测试与诊断,2007,27(1)：53-58.
[143]赵学风,段晨东,刘义艳.基于小波包变换的支持向量机损伤诊断方法.振动、测试与诊断,2008,28(2)：104-108.
[144]陈理渊,黄进.基于支持向量机的电机转子断条故障诊断.电工技术学报,2006,21(8)：48-52.
[145]S.S. Law, X.Q. Zhu. Dynamic behavior of damaged concrete bridge structures under moving vehicular loads. Engineering Structures,2004,26:1279-1293.
[146]A. Ariaei, S. Ziaei-Rad, M. Ghayour. Vibration analysis of beams with open and breathing cracks subjected to moving masses. Journal of Sound and Vibration,2009,326: 709-724.
[147]Hai-Ping Lin, Shun-Chang Chang. Forced responses of cracked cantilever beams subjected to a concentrated moving load. International Journal of Mechanical Sciences,2006, 48:1456-1463.
[148]C. Bilelloa, L.A. Bergman. Vibration of damaged beams under a moving mass: theory and experimental validation. Journal of Sound and Vibration,2004,274:567-582.
[149]S.A. Neild, M.S. Williams, P.D. McFadden. Non-linear behavior of reinforced concrete beams under low amplitude cyclic and vibration loads. Engineering Structures, 2002,24:707-718.
[150]Achmad Widodo, Bo-Suk Yang. Support vector machine in machine condition monitoring and fault diagnosis. Mechanical Systems and Signal Processing,2007,21: 2560-2574.
[151]方瑞明.支持向量机理论及其应用分析.北京：中国电力出版社,2007.
[152]高晋占.微弱信号检测.北京：清华大学出版社,2004.
[153]邹伟.大跨度连续刚构桥易损性研究.成都：西南交通大学,2009
[154]P. Cawley, r. D. Adams.Location of defects in structures from measurements of natural frequencies. Journal of strain analysis,1979,14(2):49-57.
[155]H. F. Lam, J. M. Ko, C. W. Wong. Localization of damaged structural connections based on experimental modal and sensitivity and analysis. Journal of sound and vibration, 1998,200(1):91-115.
[156]Sarunas J. Raudys, Anil K. Jain. Small Sample Size Effects in Statistical Pattern Recognition:Recommendations for Practitioners, IEEE Transactions on Pattern Analysis and Machine Intelligence.1991,13(3):252-264.
[157]Thomas M. Cover. Geometrical and Statistical Properties of Systems of Linear Inequalities with Applications in Pattern Recognition, IEEE Transactions on electronic computers.1965,EC-14:326-334.
[158]姜文瀚.模式识别中的样本选择研究及其应用.南京：南京理工大学博士研究生学位论文.2007.
[159]陈伟,王松文.25型客车及其新技术.北京：中国铁道出版社,2001.
[160]宋顺宝.客车车辆构造与检修.北京：中国铁道出版社,2003.
[161]夏禾.车辆与结构动力相互作用.北京：科学出版社,2002.
[162]屈文俊,车惠民.既有铁路混凝土桥梁的病害分析.桥梁建设,1995,4：64-68.
[163]付春雨,李乔.考虑空间整体效应的温度自应力计算.铁道学报,2007,29(6)：80-83.
[164]付春雨,李乔.平曲线箱梁在温差作用下的扭转分析.西南交通大学学报,2009,44(5)：710-715.
[165]姜静清.最小二乘支持向量机算法及应用研究.长春：吉林大学博士研究生学位论文.2007.
[166]邓乃扬,田英杰.数据挖掘中的新方法——支持向量机.北京：科学出版社,2004.